A first deficiency of existing solutions for making predictions is that existing solutions lack a comprehensive framework for considering the impact of a whole set of entities, which may influence each other, when making a prediction. Taking into account these inter-entity influences before making the prediction is lacking from existing solutions and especially desirable in the era of Social Networking where world events are becoming more and more interdependent and prediction as an isolated exercise seems to no longer be relevant. The absence of such functionality becomes more alarming when considering the volume of big data that is continuously generated on the set of entities of any domain particularly Politics, Sports, Business, Religions and other such big institutions. Consider the case where oneself, as a user of the Internet, receives general predictions on the possible business events such as: “Microsoft may acquire Yahoo”, and a separate prediction as, “Yahoo's CEO may step down”. What is missing in these two isolated business predictions are that they are influencing each other and any serious user will be more interested in getting the cumulative prediction as: Microsoft may not acquire Yahoo as Yahoo CEO may not step down. Developing such predictions of a future event based on and involving more than one entity is not present in existing solutions. Such multi-entity based predictions may be beneficial and desirable as information in the form of news and other documents often talk about more than one entity in different contexts. Making predictions by capturing interdependency contexts involving different entities, as in the above example of news involving Microsoft and Yahoo, is highly desirable but lacking from existing solutions.
A second deficiency of existing solutions for generating predictions is that existing solutions fail to present the predictions in a manner that optimizes the usefulness of the predictions. For example existing systems fail to present predictions along with declarative statements to the user as some sort of real time charts or graphs which allows a user to understand why a particular prediction has been generated and what other factors are influencing the derived predictions. Presently, user experience is on equal footing with the sophisticated computation, the deficiencies in the presentation of predictions should be addressed.
A third deficiency of existing systems is the absence of distributed processing. Existing systems fail to handle large volumes of data over the cloud regarding many (potentially millions) of entities for which a prediction may be made. If a prediction based on such potentially large volumes of data is to be made, that data may be “learned” by Machine Learning Algorithms. However, present solutions fail to provide a well co-ordinated, distributed Machine Learning Algorithms running on multi core to speed up the system and also fail to generate the real time predictions.
A fourth deficiency of existing solutions is the absence of user choice and the flexibly to alter time as per the demands (as world is becoming very dynamic) and the ability to make predictions based on a plurality of time choices. For example, present systems fail to give the user a choice of altering time scale such that new predictions can be generated based on last one month data and/or if required based on last 7 days data.
Accordingly, the existing solutions fail to provide an efficient robust framework employing distributed Machine Learning techniques thereby learning new data quickly and making reasonable predictions. The present disclosure corrects one or more of the above deficiencies of existing solutions for generating predictions.